{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with data from AnyBody"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are several ways to output data from AnyBody. The most convinient way to export a few variables from AnyBody is through the API in AnyPyTools. This is what you saw in the previous tutorials which used the 'Export' helper class to generated macros which export specific variables. \n",
    "\n",
    "Another option is to have AnyBody write specific variables to a file by adding the 'AnyOutputFile' class to the AnyBody model, or exporting an HDF5 file with all data from a simulation. In these cases AnyPyTools has methods to make it easier to get data into Python for futher analysis. \n",
    "\n",
    "Here we go through the different methods. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output data through AnyPyTools\n",
    "\n",
    "Using the AnyPyTools API directly is the easiest way to export data when you only need to export a limited number of variables.\n",
    "\n",
    "In the following we use the toy example from the \"Generating Macros\" tutorial. We crate 6 macros with different parameters and collect the result from running the simulations. Data is exported by including the `Export(\"<folder/variable name>\")` command. If a folder is specified all variables below that level is exported.\n",
    "\n",
    "> **Note:** Remember to specifiy the variables in study `.Ouput.*` folder if you need the results of a simulation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "fd48e66830094d7ebe3e14cbd86dff67",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: \u001b[1;36m6\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from anypytools import AnyMacro, AnyPyProcess, macro_commands as mc\n",
    "\n",
    "macro_list = []\n",
    "for length in [0.02, 0.03, 0.04, 0.05, 0.06, 0.07]:\n",
    "    macro = [\n",
    "        mc.Load(\"Knee.any\"),\n",
    "        mc.SetValue(\"Main.MyModel.PatellaLigament.DriverPos\", length),\n",
    "        mc.RunOperation(\"Main.MyStudy.InverseDynamics\"),\n",
    "        mc.Export(\"Main.MyStudy.Output.Abscissa.t\", \"time\"),\n",
    "        mc.Export(\"Main.MyStudy.Output.MaxMuscleActivity\", \"MaxMuscleActivity\"),\n",
    "        mc.Export(\"Main.MyModel.PatellaLigament.DriverPos\", \"PatellaLength\"),\n",
    "    ]\n",
    "    macro_list.append(macro)\n",
    "\n",
    "app = AnyPyProcess()\n",
    "results = app.start_macro(macro_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now the variable `results` is a list of dictionaries with the output from AnyBody. This is a very flexible format that can hold any kind of data we can output from AnyBody."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.04])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results[2][\"PatellaLength\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we plot this data we need to do some manual work. I.e. looping over the list and plot individual variables. \n",
    "\n",
    "However, it can be made much easier if we convert data into a Pandas DataFrame. A DataFrame is like an excel spreadsheet. Most python plotting libries can plot directly from that.\n",
    "\n",
    "> *Note:* Not all data is suitable for convertion to a DataFrame. You data needs the same type of output in every simulation. Also, a DataFrame is 2 dimensional so higher dimensional data (like vectors) are flattend with x/y/z components into seperate collumns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>MaxMuscleActivity</th>\n",
       "      <th>PatellaLength</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022982</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.024242</td>\n",
       "      <td>0.023878</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.048485</td>\n",
       "      <td>0.025233</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.072727</td>\n",
       "      <td>0.027028</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.096970</td>\n",
       "      <td>0.029236</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
       "      <th>95</th>\n",
       "      <td>2.303030</td>\n",
       "      <td>0.010652</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2.327273</td>\n",
       "      <td>0.009699</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2.351515</td>\n",
       "      <td>0.008954</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2.375758</td>\n",
       "      <td>0.008414</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.008077</td>\n",
       "      <td>0.07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          time  MaxMuscleActivity  PatellaLength\n",
       "0 0   0.000000           0.022982           0.02\n",
       "  1   0.024242           0.023878           0.02\n",
       "  2   0.048485           0.025233           0.02\n",
       "  3   0.072727           0.027028           0.02\n",
       "  4   0.096970           0.029236           0.02\n",
       "...        ...                ...            ...\n",
       "5 95  2.303030           0.010652           0.07\n",
       "  96  2.327273           0.009699           0.07\n",
       "  97  2.351515           0.008954           0.07\n",
       "  98  2.375758           0.008414           0.07\n",
       "  99  2.400000           0.008077           0.07\n",
       "\n",
       "[600 rows x 3 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "df = results.to_dataframe(index_var='time')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `index_var` specifies which variable becomes the x axis in the dataset. Other variables which doesn't have the same first dimension as the `index_var` will be repeated along that dimension. This allows you to export which are not a function of time. Those variables then become constants.\n",
    "\n",
    "Line plots only work if you have the same x axis across all simulations. Otherwise, you need to interpolate the data.\n",
    "\n",
    "Now plotting the data is really simple with a library like for example [seaborn](https://seaborn.pydata.org/):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "g = sns.lineplot(\n",
    "    data=df,\n",
    "    x=\"time\",\n",
    "    y=\"MaxMuscleActivity\",\n",
    "    hue=\"PatellaLength\",\n",
    ")\n",
    "g.legend(title=\"Patella length (m)\", loc='lower left', bbox_to_anchor=(1, 0.5));\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with AnyOutputFiles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `AnyOutputFile` class is an other methods of exporting data from AnyBody. It is a AnyScript class in the AnyBody Modeling System which produces text files with data when a simulation is run. These text files are very similar to comma seperated files with some additional header information. \n",
    "\n",
    "Here is an example below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing TestOutput.csv\n"
     ]
    }
   ],
   "source": [
    "%%writefile TestOutput.csv\n",
    "---- AnyBody Output File ---------------------------------\n",
    "Study Main.MyStudy\n",
    "Operation Main.MyStudy.InverseDynamics\n",
    "----------------------------------------------------------\n",
    "Constants (Name = Value) \n",
    "Main.MyStudy.FileOutput.ConstName = HelloWorld\n",
    "Main.MyStudy.nStep = 5\n",
    "Main.MyModel.Femur.Knee.sRel = { 0.000000000000000e+000, -3.000000000000000e-001,  0.000000000000000e+000}\n",
    "----------------------------------------------------------\n",
    "Variables (Column# Name) \n",
    "col0  Main.MyStudy.t\n",
    "col1  Main.MyStudy.MomentArm\n",
    "----------------------------------------------------------\n",
    "Main.MyStudy.t,Main.MyStudy.MomentArm\n",
    " 0.000000000000000e+000, 3.517106754087954e-002\n",
    " 6.000000000000000e-001, 4.256597756479537e-002\n",
    " 1.200000000000000e+000,-2.495531558514929e-004\n",
    " 1.800000000000000e+000, 4.256603812471121e-002\n",
    " 2.400000000000000e+000, 3.517106649790244e-002\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is not particular difficult to read. You could write you own custom Python, Matlab code to parse the values. But **`anypytools`**  has a few convinience functions that makes it very easy to load the files. \n",
    "\n",
    "This is especially usefull for the header information which can be annoying to parse manually. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from anypytools.datautils import read_anyoutputfile\n",
    "\n",
    "data, header, constants = read_anyoutputfile(\"TestOutput.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The function returns three outputs. An array with the time dependent data, and a list of header names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(['Main.MyStudy.t', 'Main.MyStudy.MomentArm'],\n",
       " array([[ 0.00000000e+00,  3.51710675e-02],\n",
       "        [ 6.00000000e-01,  4.25659776e-02],\n",
       "        [ 1.20000000e+00, -2.49553156e-04],\n",
       "        [ 1.80000000e+00,  4.25660381e-02],\n",
       "        [ 2.40000000e+00,  3.51710665e-02]]))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "header, data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and python dictonary with constant values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Main.MyStudy.FileOutput.ConstName': 'HelloWorld',\n",
       " 'Main.MyStudy.nStep': 5.0,\n",
       " 'Main.MyModel.Femur.Knee.sRel': array([ 0. , -0.3,  0. ])}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "constants"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Working with HDF5 files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes, it can be convenient to save the entire model along with all its data (although this can be several hundred megabytes). It is useful if we later want to analyze other output variables from the model. It can also be useful if we want to load the data in the AnyBody graphical user application and replay the result.\n",
    "\n",
    "AnyBody has a feature to save the output of a study to an HDF5 file. And like most things in AnyBody, this can also be done with a macro command. \n",
    "\n",
    "Let us try this with the model from the previous tutorials."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[load \"Knee.any\",\n",
       " operation Main.MyStudy.Kinematics\n",
       " run,\n",
       " classoperation Main.MyStudy.Output \"Save data\" --type=\"Deep\" --file=\"output.anydata.h5\"]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from anypytools.macro_commands import Load, RunOperation, SaveData\n",
    "\n",
    "macrolist = [\n",
    "  Load('Knee.any'),\n",
    "  RunOperation('Main.MyStudy.Kinematics'),\n",
    "  SaveData('Main.MyStudy', 'output.anydata.h5'),\n",
    "]\n",
    "macrolist"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Here we have added a \"`Save data`\" classoperation to the macro. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "931ab13c49bb4c99b78533ad32b7dcc1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Output()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Completed: \u001b[1;36m1\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"></pre>\n"
      ],
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from anypytools import AnyPyProcess \n",
    "app = AnyPyProcess()\n",
    "\n",
    "app.start_macro(macrolist);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data stored in the file **output.anydata.h5** can be re-loaded in the AnyBody GUI application. \n",
    "\n",
    "To do this; load the model, and then right click the `Main.MyStudy.Output` folder and select \"Load data\". \n",
    "\n",
    "These files can also be loaded into Matlab or Python. In python this is done using the *`h5py`* module\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.03517107 0.03518544 0.03522538 0.03529129 0.03538355 0.03550231\n",
      " 0.03564761 0.03581929 0.03601707 0.03624048 0.03648895 0.03676178\n",
      " 0.03705816 0.03737724 0.03771812 0.03807991 0.03846173 0.03886281\n",
      " 0.03928244 0.03972002 0.0401751  0.04064731 0.04113638 0.04164206\n",
      " 0.04216406 0.04270195 0.04325503 0.04382223 0.04440197 0.04499208\n",
      " 0.04558969 0.04619126 0.04679259 0.04738895 0.04797527 0.04854637\n",
      " 0.0490972  0.04962319 0.05012044 0.0505859  0.051017   0.0514114\n",
      " 0.05176698 0.0520819  0.05235456 0.05258365 0.05276812 0.05290715\n",
      " 0.05300015 0.05304675 0.05304675 0.05300015 0.05290715 0.05276812\n",
      " 0.05258365 0.05235456 0.0520819  0.05176698 0.0514114  0.051017\n",
      " 0.0505859  0.05012044 0.04962319 0.0490972  0.04854637 0.04797527\n",
      " 0.04738895 0.04679259 0.04619126 0.04558969 0.04499208 0.04440197\n",
      " 0.04382223 0.04325503 0.04270195 0.04216406 0.04164206 0.04113638\n",
      " 0.04064731 0.0401751  0.03972002 0.03928244 0.03886281 0.03846173\n",
      " 0.03807991 0.03771812 0.03737724 0.03705816 0.03676178 0.03648895\n",
      " 0.03624048 0.03601707 0.03581929 0.03564761 0.03550231 0.03538355\n",
      " 0.03529129 0.03522538 0.03518546 0.03517104]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import h5py\n",
    "\n",
    "h5file = h5py.File('output.anydata.h5', \"r\")\n",
    "data = np.array( h5file['/Output/MomentArm'] )\n",
    "h5file.close()\n",
    "print(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data structure of the HDF5 files can, unfortunately, be very confusing. AnyBody does not save duplicate copies of the same data. If there are multiple references to the same folder, only one will be present in the HDF5 file. \n",
    "In our model `Knee.any` we have a reference to the **`Knee`** joint folder just before the **`Model`** folder in the study section. Thus, all variables inside the **`Knee`** folder cannot be accessed with the path '/Output/Model/Knee/...', but only through the path of the reference '/Output/kneeref/...'.\n",
    "\n",
    "We can see the problem in the following code snippet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n",
      "False\n"
     ]
    }
   ],
   "source": [
    "with h5py.File('output.anydata.h5', \"r\") as f:\n",
    "    print('/Output/Model/Knee/Pos' in f)\n",
    "    print('/Output/kneeref/Pos' in f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This makes it difficult to find the correct path in large models with many references. AnyPyTools contains a wrapper for the h5py module, which automatically locates the right data, no matter what path is used. Using this module, we can easily locate the data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "import anypytools.h5py_wrapper as h5py2\n",
    "with h5py2.File('output.anydata.h5', \"r\") as f:\n",
    "    print('/Output/Model/Knee/Pos' in f)\n",
    "    print('/Output/kneeref/Pos' in f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The h5py wrapper will also let us use the AnyScript variable names directly, so we don't have to replace every . (dot) with a / (slash), and remove the stuff before the Output folder. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "with h5py2.File('output.anydata.h5', \"r\") as f:\n",
    "    momentarm = np.array(f['/Output/MomentArm']) # Standard h5py notation\n",
    "    momentarm = np.array(f['Output.MomentArm'])  # dot notation\n",
    "    kneeangle = np.array(f['Main.MyStudy.Output.Model.Knee.Pos']) # dot notation with full path\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from numpy import degrees\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "plt.plot(degrees(kneeangle), 100*momentarm)\n",
    "\n",
    "plt.xlabel('Knee flexion (deg)')\n",
    "plt.ylabel('Moment arm (cm)');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "7840ed1e0d8c0fa6141b9782bb3c547b5e5fa4178e680f89033f8188680e7ee3"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.14.0"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
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